Personalized care aims to deliver effective treatment tailored to an individual’s clinical, molecular and genetic profile. It could be a magic bullet that cures the woes of healthcare by providing more accurate, effective therapy (and by default, reducing the use of treatments that are likely to be ineffective), and thus better targeting, and potentially trimming, overall costs.
The studies highlighted in this month’s advanced visualization portal illustrate the role of this technology in personalized medicine.
Stroke care, for example, represents a tremendous burden in the U.S, with annual costs hovering near $20 billion. The lion’s share of these costs stems from rehabilitation and long-term care fees, which can exceed $100,000 per individual annually. Effective treatment in the form of thrombolytic therapy often slashes these costs because patients may return home.
The snag is identifying patients eligible for treatment, as it must be administered with a 4.5 to six hour time window, and approximately 25 percent of stroke patients present with an unknown onset time.
In a study published online Oct. 5 in The Lancet: Neurology, a team of researchers determined that a mismatch between diffusion-weighted MRI (DWI) images and fluid-attenuated inversion recovery MRI (FLAIR) images may indicate that a patient is within the time window for safe and effective administration of thrombolytic therapy.
The data were quite compelling and spurred the authors to recommend a prospective clinical trial to begin testing the role of DWI and FLAIR imaging and thrombolysis among patients with an unknown time of stroke onset.
Equally provocative is the prospect of an individualized tissue window, referred to in the accompanying editorial. That is, for some patients known to be within the standard time window for thrombolytic therapy, treatment will be ineffective. Conversely, treatment may be effective for some individuals who are well beyond the one-size-fits-all time window.
The editorialists hypothesized that the DWI-FLAIR mismatch might help identify individuals with salvageable brain tissue eligible for therapy, regardless of the time of onset. It could deliver on the promise of personalized medicine, extending therapy to a wider group of patients, and potentially bypassing or reducing long-term care and rehabilitation costs.
Similarly, in the October issue of Radiology, a team of researchers from the Cancer Research-UK and EPSRC Cancer Imaging Centre in Surrey, England, indicated that quantitative MRI data could determine which patients with advanced ovarian cancer are responding to chemotherapy. These data could help patients who are not benefiting from therapy avoid the toxicity, side effects and costs associated with a treatment that is not working.
How is your organization leveraging advanced visualization to deliver on the promise of personalized medicine? Let us know.
Lisa Fratt, editor